RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA
TAO Pei-feng1, WANG Jian-hua2,3,4, LI Zhi-zhong2,3, ZHOU Ping1, YANG Jia-jia2,3, GAO Fan-qi5
1. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China; 2. International Black Soils Society, Shenyang 110034, China; 3. Shenyang Center of Geological Survey, CGS, Shenyang 110034, China; 4. Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094; 5. School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Abstract：In order to quickly test the soil nutrient contents (SOM, TN, TP and TS), the authors collect 117 soil samples at 0-20 cm depth from Chuangye Farm in Jiansanjiang reclamation area as research objects. First derivative (FD), logarithmic reciprocal (RL), first derivative of reciprocal (FDR), multivariate scattering correction (MSC) and continuum removal (CR) transformations are performed on the raw spectral reflectance (R). By analyzing the correlation between the six spectral variables and soil nutrient content, the bands that are significantly correlated at the α=0.01 level are adopted as characteristic bands, and the methods of stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and back propagation neural network (BPNN) are used respectively to establish hyperspectral prediction model of SOM, TN, TP and TS. The model is evaluated by R2, RMSE and RPD. The results show that the soil nutrient content prediction models established by PLSR and BPNN are superior to that by SMLR. The PLSR and BPNN methods can well predict the organic matter and total nitrogen content, and roughly estimate the total sulfur content. Only the CR-BPNN method can roughly estimate the total phosphorus content. The models with the best prediction effect on SOM, TN, TP and TS are, respectively, MSC-PLSR, MSC-PLSR, CR-BPNN and FDR-BPNN, with the validation set determination coefficients of 0.86, 0.75, 0.56 and 0.67 respectively.
陶培峰, 王建华, 李志忠, 周萍, 杨佳佳, 高樊琦. 基于高光谱的土壤养分含量反演模型研究[J]. 地质与资源, 2020, 29(1): 68-75,84.
TAO Pei-feng, WANG Jian-hua, LI Zhi-zhong, ZHOU Ping, YANG Jia-jia, GAO Fan-qi. RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA. GEOLOGY AND RESOURCES, 2020, 29(1): 68-75,84.
Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal, 1986,50(1):120-123.
[3]
Conforti M, Buttafuoco G, Leone A P, et al. Studying the relationship between water-induced soil erosion and soil organic matter using Vis NIR spectroscopy and geomorphological analysis:a case study in southern Italy[J]. CATENA, 2013,110:44-58.
Zhang T T, Li L, Zheng B J. Estimation of agricultural soil properties with imaging and laboratory spectroscopy[J]. Journal of Applied Remote Sensing, 2013,7(1):073587.
2020, Vol. 29 
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